The nature of statistical learning theory
The nature of statistical learning theory
A semi-automatic approach to home video editing
UIST '00 Proceedings of the 13th annual ACM symposium on User interface software and technology
Detection and removal of lighting & shaking artifacts in home videos
Proceedings of the tenth ACM international conference on Multimedia
A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
Boosting Image Orientation Detection with Indoor vs. Outdoor Classification
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Learning No-Reference Quality Metric by Examples
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Optimization-based automated home video editing system
IEEE Transactions on Circuits and Systems for Video Technology
Personal media sharing and authoring on the web
Proceedings of the 13th annual ACM international conference on Multimedia
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Proceedings of the 15th international conference on Multimedia
Video summarisation: A conceptual framework and survey of the state of the art
Journal of Visual Communication and Image Representation
Example-based video remixing support system
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Hi-index | 0.00 |
Compared with the video programs taken by professionals, home videos are always with low-quality content resulted from lack of professional capture skills. In this paper, we present a novel spatio-temporal quality assessment scheme in terms of low-level content features for home videos. In contrast to existing frame-level-based quality assessment approaches, a type of temporal segment of video, sub-shot, is selected as the basic unit for quality assessment. A set of spatio-temporal artifacts, regarded as the key factors affecting the overall perceived quality (i.e. unstableness, jerkiness, infidelity, blurring, brightness and orientation), are mined from each sub-shot based on the particular characteristics of home videos. The relationship between the overall quality metric and these factors are exploited by three different methods, including user study, factor fusion, and a learning-based scheme. To validate the proposed scheme, we present a scalable quality-based home video summarization system, aiming at achieving the best quality while simultaneously preserving the most informative content. A comparison user study between this system and the attention model based video skimming approach demonstrated the effectiveness of the proposed quality assessment scheme.